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Google Titans architecture, helping AI have long-term memory

(research.google)
584 points Alifatisk | 3 comments | 07 Dec 25 12:23 UTC | HN request time: 0.491s | source
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kgeist ◴[07 Dec 25 14:57 UTC] No.46182122[source]▶
>The model uses this internal error signal (the gradient) as a mathematical equivalent of saying, "This is unexpected and important!" This allows the Titans architecture to selectively update its long-term memory only with the most novel and context-breaking information

So one can break a model by consistently feeding it with random, highly improbable junk? Everything would be registered as a surprise and get stored, impacting future interactions

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bethekidyouwant ◴[07 Dec 25 16:00 UTC] No.46182651[source]▶
In what world can you not always break the response of an AI by feeding it a bunch of random junk?
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1. CooCooCaCha ◴[07 Dec 25 16:12 UTC] No.46182745[source]▶
I mean ideally AI would be resilient to junk, don't you think?
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2. vlovich123 ◴[07 Dec 25 16:19 UTC] No.46182820[source]▶
Humans are pretty vulnerable to junk so I’m not sure.
3. amarant ◴[07 Dec 25 19:05 UTC] No.46184144[source]▶
Ideally, you'd run your own instance of this, I think.

I can see a product where you purchase a model that has basic training, and then, using the features outlined in the paper, it learns on the fly from your usage.

I can also see there being a secondary market for specially trained models, long-term memory filled with some specific skill, done in some specific way. To make a silly example, imagine buying a licence to Torvald's OS coding assistant, ready to insult your prs before you even commit them!(And possibly help you write code in Torvald's style too)

This would of course require Linus to use the model enough for it to learn,I won't comment on the likelihood of that happening: it's just a silly example after all